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      Evidence of Dengue Virus Transmission and Factors Associated with the Presence of Anti-Dengue Virus Antibodies in Humans in Three Major Towns in Cameroon

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          Abstract

          Background

          Dengue is not well documented in Africa. In Cameroon, data are scarce, but dengue infection has been confirmed in humans. We conducted a study to document risk factors associated with anti-dengue virus Immunoglobulin G seropositivity in humans in three major towns in Cameroon.

          Methodology/Principal Findings

          A cross sectional survey was conducted in Douala, Garoua and Yaounde, using a random cluster sampling design. Participants underwent a standardized interview and were blood sampled. Environmental and housing characteristics were recorded. Randomized houses were prospected to record all water containers, and immature stages of Aedes mosquitoes were collected. Sera were screened for anti-dengue virus IgG and IgM antibodies. Risk factors of seropositivity were tested using logistic regression methods with random effects.

          Anti-dengue IgG were found from 61.4% of sera in Douala (n = 699), 24.2% in Garoua (n = 728) and 9.8% in Yaounde (n = 603). IgM were found from 0.3% of Douala samples, 0.1% of Garoua samples and 0.0% of Yaounde samples. Seroneutralization on randomly selected IgG positive sera showed that 72% (n = 100) in Douala, 80% (n = 94) in Garoua and 77% (n = 66) in Yaounde had antibodies specific for dengue virus serotype 2 (DENV-2).

          Age, temporary house walls materials, having water-storage containers, old tires or toilets in the yard, having no TV, having no air conditioning and having travelled at least once outside the city were independently associated with anti-dengue IgG positivity in Douala. Age, having uncovered water containers, having no TV, not being born in Garoua and not breeding pigs were significant risk factors in Garoua. Recent history of malaria, having banana trees and stagnant water in the yard were independent risk factors in Yaounde.

          Conclusion/Significance

          In this survey, most identified risk factors of dengue were related to housing conditions. Poverty and underdevelopment are central to the dengue epidemiology in Cameroon.

          Author Summary

          General awareness of dengue fever in Africa, and particularly in Cameroon, is weak. Many acute febrile illnesses are considered as malaria, although not laboratory confirmed, and the diagnosis of dengue fever is seldom evoked while its laboratory confirmation is even more seldom obtained. On the basis of anti-dengue virus IgG seropositivity in humans, our survey demonstrated that dengue virus transmission occurred in the three main towns of the country. Although the findings varied according to the location, identified risk factors of anti-dengue virus seropositivity were commonly related to housing conditions. Taking into account the risk factors identified in Douala, practical ways to lower the risk of dengue virus transmission are long term development and improvement of sanitation and education. We concluded that poverty and underdevelopment are central to the problem of dengue virus transmission in urban areas in Cameroon.

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          Most cited references 40

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          The Role of Human Movement in the Transmission of Vector-Borne Pathogens

          Introduction For vector-borne pathogens heterogeneity in patterns of contact between susceptible hosts and infectious agents is common [1],[2],[3]. Some hosts will be exposed to, harbor, and pass on more parasites than others. Variation in contact patterns can amplify [4],[5] or dampen [6] the rate of transmission, even as it also potentially reduces disease prevalence and epidemic stability (i.e., likelihood of an outbreak; [7]). Understanding and describing what drives heterogeneous contact patterns is thus important for designing improved disease surveillance and prevention programs [3]. If the characteristics of hosts most often infectious or important for transmission are known they could be targeted to more efficiently prevent disease [8]. To be useful for targeted control across different contexts the mechanisms underlying heterogeneous contact patterns must be elucidated. Here we examine the role of individual human movement as a critical behavioral factor underlying observed patterns of vector-borne pathogen transmission, because movement determines exposure to infectious agents; i.e., bites from infected mosquito vectors. Little is known about individual human movement patterns and even less about their epidemiological consequences, even though such knowledge would be a valuable contribution to the understanding and control of many vector-borne diseases. We begin our investigation of this topic by reviewing studies of human movement. Next, based on an existing typology, we examine the relevance of movement patterns to the dynamics of different diseases. Using the mosquito-borne virus dengue as an example, we develop a conceptual model that illustrates how human and vector behavior can influence pathogen transmission dynamics. We end by outlining key issues important to the design of future research and explaining potential benefits to disease prevention of an improved understanding of host movement. A Framework: Movement and Scale Historically epidemiologists have viewed human movement from the perspective of populations of susceptible hosts moving into high risk areas or infected hosts moving into susceptible populations as explanation for disease occurrence and spread. Indeed, across different scales and diseases, movements of hosts affect pathogen transmission in a variety of ways. Thirty years ago Prothero [9] provided a typology to facilitate study of the role of human movements in epidemiology based on his experience in Africa. Drawing on geography literature concerned with understanding human movement [10],[11],[12], Prothero highlighted the difference between circulatory movements, where individuals return home after some period, and migratory movements, which tend to be permanent changes of residence (see Figure 1 in [11]). He further characterized movements by their ‘spatial scale’, which he categorized in terms of a rural-urban gradient, and temporal scale based on the time and timing of displacements. He qualified these categories in terms of their relevance to public health. For instance, seasonal movements from one rural area to another for agriculture could potentially expose individuals to different ‘ecological zones’ where the risk of malaria or African trypanosomiasis is high [13]. His argument was that knowing something about the nature of such movements would help explain the incidence and prevalence of disease in a population and provide informed options for control [9]. In Figure 1 we generalize Prothero's typology in terms of the spatial and temporal scale (sensu [14]) of human movement and extend it to include most vector-borne disease contexts. 10.1371/journal.pntd.0000481.g001 Figure 1 A framework for human movements and their relevance to vector-borne pathogen transmission. Movements are characterized in terms of their spatial and temporal scale, which are defined in terms of physical displacement (Δxy) and time spent (Δt, frequency and duration). Generally, movements of greater spatial displacement involve more time, but this is not necessarily always the case. At broad spatial scales (e.g., national, international) individual movements drive pathogen introduction and reintroduction (far right, Figure 1). Global spread of dengue virus via shipping routes was characterized by periodic, large, spatial displacements [15]. Globalization and air transportation have changed the dynamic of pathogen spread by dramatically shortening the time required to travel around the globe [16],[17],[18]. The recent chikungunya epidemic in the Indian Ocean that subsequently spread to Italy is an example [19]. At finer scales (e.g., regional, urban-rural, intra-urban; far left of Figure 1), movement associated with work, recreation, transient migration, and other phenomena is important to patterns of pathogen transmission and spread [9],[20]. Movements into high-risk areas not only lead to individual infection, but can also contribute to local transmission when infected hosts return home and infect competent vectors. For example, in the Chocó region of Colombia most malaria transmission occurs in rural areas and many cases diagnosed in the city of Quibdó are due to travel to these areas [21]. Transmission also occurs locally within Quibdó [22], however, most likely because of infected travelers returning and infecting competent vectors. Understanding the origin of infections and the relative importance of human movement at different scales to both local and regional transmission dynamics would increase effectiveness of disease prevention programs by, for example, identifying individuals at greatest risk of contracting and transmitting pathogen. Generally, a key significance of human movement for vector-borne disease at any scale lies with exposure to vectors. Exposure is local in space and time and variation in exposure due to individual host movement could strongly influence the transmission dynamics of pathogens. For instance, circulatory movements associated with working in rural areas and variation in movement patterns among cultures may explain heterogeneous patterns of onchocerciasis incidence. While men in Cameroon and Guatemala both experience similar parasite loads reflecting exposure to vectors when working in fields, women in the 2 countries show different patterns of infection partly due to differences in exposure [23]. The type of movement most relevant for exposure will depend on site specific differences, the ecology of the arthropod vector, human behavior, and the relative scale of host and vector movement. For pathogens transmitted by vectors able to move long distances in search of a host, fine scale host movements may not be important, while they are for pathogens transmitted by sessile vectors. Aedes aegypti—the principal vector of dengue virus—bites during the day [24], disperses only short distances [25] and is heterogeneously distributed within urban areas [26],[27]. Conversely, humans move frequently at local scales (bottom-left of Figure 1), allocating different amounts of time to multiple locations on a regular basis. This will influence individual risk of infection with dengue virus [28] and thus overall patterns of transmission [29],[30],[31]. Methods The dynamics of human movement, the locations used and the paths between them, is conceptualized by the ‘activity space’ model developed in the 60's by human geographers [12],[32],[33]. This model, much like the ‘home-range’ concept in ecology, is effective because organisms exhibit habitual behavior in their use of space [34]. For our purposes of studying dengue, the ‘activity space’ refers to those few locations where humans commonly spend most of their time [32],[35] and ‘movement’ refers to the use of these locations. Thus, exposure to host-seeking female Ae. aegypti is the sum of exposure across an individual's activity space. For other vectors and pathogens, human movements per se (e.g., walking between the house and a water source) and/or visits to less common destinations could be relevant for the transmission of other pathogens (e.g., African trypanosomiasis) depending on the behavior of the vector and the relative scales of vector and host movement. The activity space model represents movement associated with the regular activity of individuals [36]. We present a version of this model in Figure 2 for understanding how movements within an urban area might contribute to risk of exposure. Risk at locations within an individual person's activity space will vary depending on the number of infected, host seeking vectors present and their biting behavior. For instance, visits to locations during the day are of minimal risk for bites from nocturnal An. gambiae, but are relatively high for day active Ae. aegypti (Figure 2). Exposure to vector bites may also depend on how long a person stays at a given location. If vectors are stimulated by the arrival of an individual to a location (as may be the case for Ae. aegypti and Aedes albopictus [37],[38]), then a bite is most likely to occur early after arrival (i.e. the cumulative probability of a bite during a visit, e(t), accumulates rapidly). Alternatively, for vectors like triatomine bugs, which are less opportunistic than mosquitoes, long visits will be expected to pose a higher risk of host-vector contact (e(t) slowly accumulates over time). How vectors respond to hosts arriving at a site is important because it weights the risk of visits differently depending on their frequency and duration. If a vector is stimulated to host seek by the arrival of a host, then multiple short visits to that site will carry greater risk than a single long visit of equivalent total duration. 10.1371/journal.pntd.0000481.g002 Figure 2 The activity space model. Space is plotted in the xy plane and time on the z axis. In this example daily movements for a week are represented. Points in the xy plane are sites visited and the polygon depicts the activity area. Vertical arrows indicate time spent at a site. Thickness of arrows indicates frequency of visitation and length shows duration. Red arrows are for the home and here we assume a person is in the home every night of the week. Dashed lines represent movement between sites with velocity indicated by the angle of the line. Grayed-out regions of the cube represent night-time. Not shown is variation in vector abundance among sites. Plotted along the back axis for time are representative curves of biting rates, a(t), for Ae. aegypti (green), a day biting mosquito, and Anopheles gambiae (black), a night biting mosquito. Plotted to the right of the large black arrow is a cumulative biting probability, e(t), as a function of time spent in the location. See text for more detail. In summary, a person's risk of exposure to an infective vector can be represented with a simple exposure model for indirectly transmitted disease: (1) Here, the risk of exposure (i.e., being bitten by a vector) for individual i, ri , over some observation period is simply the sum across sites visited, j, of vector abundance, Vj , conditioned on the time and duration of all visits to that site, k, as determined by vector behavior (where K is the total number of visits during the observation period). The biting rate, ak , is the number of bites expected per visit and is drawn from the day biting rate distribution for the times of the visit. (2) How vectors respond to the appearance of a host at a site is captured by ek , the cumulative probability of a bite given the time spent in the site, and is bounded by the unit interval. (3) Visits, k, are defined by an arrival time, t0 , and a departure time, t1 , in hours and are in reference to a single day. At the limit (where t1 −t0  = 24 hours), ak becomes the day biting rate, a, and ek goes to 1 and we recover the model often assumed for vector-borne diseases where exposure occurs in the household. Note that although we imply here that a site comprises a household or other edifice because of our focus on dengue, in truth it simply demarcates a location where the abundance and activity of vectors is independent of other locations and is defined by the scale of vector movement. Site-specific exposure risk is calculated as: (4) and has units of bites*humans for the observation period. Note that in this formulation, risk among individuals using the same site is assumed to be independent (i.e., the expected number of bites at a site is the product of humans present and vector activity). This may not be realistic if hosts occupy a site at the same time, which would be expected to dilute the number of bites individual hosts receive, and can be corrected (see below) by incorporating the actual amount of time individual humans spend in a location. The estimate of risk, rj , can be used to estimate the transmission rate, R0 , which is the number of secondary infections expected from the introduction of a single infective individual into a wholly susceptible population. Woolhouse et al. (1997) use the following approximation for R0 : (5) where vj is the proportion of vectors at site j, hj is the proportion of hosts living in site j, and J is the total number of sites. Risk as estimated above is incorporated by replacing vj with site associated risk, rj , discounted by the proportional use of that site within some interval by people, hj : (6) For example, if a site is used by 2 individuals for 6 hours each over a week, hj  = (2 humans * 6 hours)/(24 hours/day * 7 days) = 0.07 humans. The activity space model elaborated here illustrates that host and vector behavior are very important for determining who gets bitten and has the greatest risk of contracting or transmitting a pathogen. Results The activity space model when coupled with our knowledge of vector behavior provides a tool for determining what human movements are important for transmission (e.g., Figure 1). Specifically, it allows us to identify places and individuals that contribute disproportionately to pathogen transmission dynamics. For example, consider the following scenario depicted in Figure 3 for a human population at risk for dengue virus infection like the one we are studying in Iquitos, Peru (Figure 3, Text S1 and Table S1). Briefly, individuals spend their time at a number of different sites, both commercial and residential, during their regular weekly activities (Sites, first column in Figure 3). Sites have different numbers of female mosquitoes and are visited at different rates and for different durations. We can estimate the risk of exposure to host-seeking female mosquitoes (ri ) for each person (columns 1–13 in Figure 3) at each site (rows in Figure 3) and then estimate R0 . In this particular example, R0 as approximated when accounting only for the home (eq. 5) is 1.3 and the site with the highest estimated risk is house 5 (in bold in column under R0 ). If we account for exposure at all locations in addition to the home and assume the biting rate at night is 10% of the rate during the day [39], our estimate of R0 (eq. 6) jumps nearly 3-fold and the most important site is 13, a clinic (in bold under R0e). This latter result arises because of the relatively large number of bites per person expected at that site, determined largely by the significant amount of time a single person spends there (e.g., their workplace). In this example, all individuals except individual 10 experience the greatest exposure to bites in their homes because that is where they spend the most time. Individual 10, however, experiences the highest risk at site 4, which represents their workplace. This individual is also at the greatest risk in the host population. 10.1371/journal.pntd.0000481.g003 Figure 3 Example scenario of risk of exposure due to individual movements. Individuals (i, represented by columns) live in and visit a number of sites (j, rows) for different durations and frequencies during a regular week. Each site is infested with a number of female mosquitoes, V. Grey shading indicates the home of each individual. Risk of a mosquito bite, ri , is calculated as described in the text and is presented here for each individual given the number of visits and time spent at different locations during a typical week. Numbers in bold are maxima for each column. Here the probability of a mosquito bite at night (in the home) is assumed to be 10% of all other times. The sum of individual risk is shown along the bottom of the figure. Overall transmission rate estimated without, R0 , and with exposure, R0 e, considered are shown in the bottom-right and underlined. See Text S1 and Table S1 for further details. This example illustrates that the key sites are not necessarily those of greatest vector abundance, as is commonly assumed. For this example scenario, R0j increases monotonically with vector abundance when transmission is assumed to occur only in the home (Figure 4). When exposure rates are accounted for, however, there is no relationship between R0j and vector abundance (Figure 4). Similarly, people living where vector abundance is greatest are not necessarily at greater risk. Human movement and subsequent variation in exposure thus becomes more important than vector density per se. Because heterogeneity in contact patterns has a large influence of the rate of pathogen transmission, variation in exposure rates due to individual movement patterns could have considerable impact on disease dynamics [40],[41]. 10.1371/journal.pntd.0000481.g004 Figure 4 Estimates of R0 plotted against vector density at sites. R0 is calculated assuming exposure occurs only within homes, R0 e is calculated taking exposure rates into account based on representative activity patterns of several hypothetical individuals living in a community like Iquitos, Peru, where we are studying dengue transmission (Figure 3). Discussion To fully understand the implications of movements, however, data should be incorporated into network, individual-based or metapopulation models [5],[42],[43]. Network models, in particular, capture heterogeneity explicitly and intuitively, allowing precise prediction of trends and patterns in human infection and disease [44]. For dengue, one imagines a dynamic network of individuals most likely to become infected or infect mosquitoes and of locations where transmission is most likely to occur [29]. These are the key nodes of pathogen transmission that, if identified and understood, would be excellent targets for intervention (e.g., [8]). The value of estimating actual exposure rates and incorporating these into models to better understand pathogen dynamics is clear for dengue, which is mostly transmitted when people are engaged in daily activities [29]. For this reason we are currently monitoring human movements in Iquitos, Peru. The activity space model as we describe it, however, highlights that movements may be important for the transmission of many pathogens typically thought to be transmitted at night when hosts are inactive. Sand fly vectors of American visceral leishmaniasis are active at dusk [45], move short distances [46], and are heterogeneously distributed among homes [47], which, in combination with human behavior, may be key to understanding leishmaniasis incidence patterns [48],[49]. Similarly, Michael et al. [50] found that 27% of Culex quinquefasciatus resting within households had fed on hosts from outside that home despite its nocturnal habit, with implications for transmission of lymphatic filariasis. There are thus many reasons for increased examination of individual human movement patterns. Measuring Movements As an aid to future research, in the remainder of this article we discuss key issues and considerations for designing studies of human movement based on our experiences with dengue. Spatial scale The first question to ask when one seeks to measure human movements and evaluate their role in pathogen transmission concerns spatial scale. This can be determined by the disease dynamic of interest; e.g., spread of a pathogen to new geographic areas vs. sustained transmission at a given locale. If the question concerns local transmission, then relevant movements will be those placing susceptible hosts in high risk locations at times when infection risk is high. General information about a particular system may guide this process. Assumptions regarding the importance of movements should be made cautiously because heterogeneity in exposure can have a dramatic effect on infection risk. Type of movement Next, one should ask what to measure. The term ‘movement’ is used somewhat ambiguously. Are we interested in just the sites where individuals spend their time on a regular basis (high spatial and temporal resolution) or whether they are in the home/city or elsewhere? Do we want travel information (outside of an urban area) that specifies exactly where people go or just a general notion? Are specific routes important, or should only destinations be considered? These details will, again, depend on the question, system, and resources and methods for measuring movements. Where we work in Peru, dengue transmission is primarily focused in urban areas of Iquitos and the mosquito vector, Ae. aegypti, is not found in the majority of rural areas outside of the city. As such, we are comfortable excluding movements to rural areas because people are unlikely to be infected there. We only need know that they were not in Iquitos, and where they were is only important if that location has dengue as well. If we were studying malaria, we might do the opposite and ignore movements within urban Iquitos where malaria is not transmitted. In our study of local dengue transmission we want high spatial and temporal resolution because Ae. aegypti cluster at the scale of individual households and bite during the day [26]. For malaria, regional movements to and from fishing or logging camps are a likely dynamic driving transmission patterns and simply knowing to which camps individuals move to on a periodic or seasonal basis and the routes taken should be sufficient to understand the spatial dynamics of that disease (G. Devine, personal communication). Measurement method A third question concerns how to measure movements. A number of methods and technologies are currently available that allow tracking of individual movements (Table 1). The choice of the appropriate method is dependent on the scale of the study and the disease in question. If the scale of interest is broad, then data from transit networks may be suitable, as has been done in studies of the global spread of SARS and influenza [17],[51],[52]. For finer scales, lack of appropriate means for measuring movements is one reason so little has yet been done in a rigorous, quantitative fashion (Vasquez-Prokopec et al. unpublished). The technology has long been available in some form, but has proved too cumbersome and expensive for large scale use with humans. Indirect devices commonly used in the social sciences, such as travel diaries, are a good source of information when used rigorously, but have seen limited use in the study of indirectly transmitted pathogens, perhaps because of inherent bias and imperfect recall. 10.1371/journal.pntd.0000481.t001 Table 1 Methods for measuring human movement. Method Description Pros Cons Ideal use Recall Commonly used in studies of exercise and physical activity, in diary or close-ended formats Captures both quantitative and qualitative information; used internationally in chronic disease research. Subject to memory decay, social desirability, and other response biases. Have been used primarily in developed countries. Not as primary outcome but to validate and inform electronic instrumentation and other more objective measures Telemetry Commonly used in wildlife studies, involves a transmitter placed on an individual and antennas (fixed or mobile) for locating the transmitter. Can be inexpensive, long battery life of transmitters, well established method, range dependent. Short range, Difficult to get precise location information, expensive for large scale use (i.e. establishing an array of antennas), interference in urban areas. Wildlife diseases, not practical for use with humans. RFID Radio Frequency Identification Device, used to track inventories, individuals in hospitals. Involve a small ‘tag’ and an antenna to detect tag. Tag is very small, easy to wear, and battery lasts a very long time. Short range, requires network of antennas to track movements in an area, which can become expensive. Very good option for tracking movements to and from predefined locations, e.g., for movements to commonly used water sources. GPS Global Positioning System. Global, satellite-based, location aware system. Only requires a receiver, works everywhere, provides exact positional information, devices are becoming very small and inexpensive. Large data post-processing requirement, short battery life, custom devices are expensive while commercial options not tailored to research use. Reductions in cost and device size make GPS the best option for tracking movements where cellular phone use is not universal. GSM-GPS GSM assisted GPS. Devices use the GSM cellular network to improve the satellite signal and provide positional information when satellites are out of reach due to interference. Same as GPS with the additional benefit of location information inside buildings and other places the satellite signal cannot reach. Additional positional information depends on cellular network, feature requires data transmission, network fees and arrangements necessary, very short battery life. Because the additional advantage of these devices relies on a cellular network, either GPS or cellular phones will often be better options. Cellular phone The position of cellular phones can be approximated through triangulation using the cellular network. Where cellular phone use is universal, movement data can acquired from network providers without any inconvenience to study participants. Potential for bias (positions are recorded when phones are used), low spatial precision, requires network agreement, privacy issues, most individuals need personal phones. For large scale studies of the collective dynamics of populations, regional movements and movements within large metropolitan areas Cellular phone, AGPS Assisted-GPS on cellular phones works by the same mechanism as GSM-GPS, utilizing the cellular network to assist in acquiring positional information. High spatial precision, potential for high coverage where cellular phone use is common, no need to purchase devices. Dependent on cellular network, requires data transmission, may require custom software or other means to acquire data while avoiding privacy issues. Can be very expensive without a special arrangement with a network provider. Most useful for studying movements in developed countries were cellular network coverage is high and most people have personal phones. Also good for urban areas where GPS signal is imperfect. All available technologies have pros and cons (Table 1). GPS has often been considered to measure exposure, but because of cost, size, battery life, and other technical limitations has yet to be used incisively to study human movements (Vasquez-Prokopec et al. unpublished). Cellular phones hold promise where the technology is available and use is universal (e.g., [35]), but are awkward to use for prospective studies and in low-resource settings and come with privacy issues. GPS seems to hold the greatest potential for the combination of low cost, ease of use, spatial accuracy, and fewer privacy issues than cellular phones because only location information is recorded. We are currently using a GPS device in Iquitos, Peru that weighs less than 25 g, records for >3 days continuously, and is under $50 (Vasquez-Prokopec et al. unpublished). Size and battery life of tracking devices are critical in human studies because they are key to acceptance by participants for long term use (minimizing coverage bias, Paz Soldan et al., unpublished, [53]). Except for cellular phones owned by participants, any currently available device is only useful for prospective investigation. To evaluate the role of movements on disease dynamics retrospectively–that is, after identifying an infected individual–the options are limited. Cellular phones may be useful in certain contexts: e.g., where the technology is accepted and widely used (Table 1). Otherwise, instruments reliant on recall such as diaries, questionnaires, or interviews are required. These methods are imperfect, yet can provide valuable information when coupled with other tools. For instance, Geographic Information Systems permit production of detailed maps for a region that can be used to elicit recall of visits to certain sites (Paz Soldan et al. unpublished). Recall instruments should be sensitive to the local social and cultural contexts. As such, active collaboration with social scientists versed in the local culture is critical for the development of an interview device with sufficient sensitivity. Technologies such as GPS can be used to facilitate development of a recall instrument and to validate it. Location aware technologies, however, are not a gold standard for measuring movement because of precision and accuracy limitations, problems of compliance and use (Paz Soldan et al. unpublished), and other factors that can disrupt tracking (e.g., interference from buildings). Moreover, GPS does not indicate an individual's activity, which could be critical for determining risk [e.g., did they enter the house or stay on the sidewalk? 53]. Combining objective (e.g., GPS) and recall methods may be the best way to efficiently follow individual movements on a large scale and to qualify those movements with regard to disease risk. Observation interval A fourth question concerns how long to observe individual movements. The answer will depend on the question being asked and available resources. In the case of dengue, infection can occur up to 2 weeks prior to the manifestation of symptoms. For a retrospective study, 14–15 days would be the right observation period. Conversely, in a prospective study the length of the observation period will depend on the relative importance of rare movements. Studies of human movements in developed societies reveal markedly regular patterns, especially during the work-week [32],[35],[54],[55],[56]. Conversely, there may be significant instability in movements on weekends or at other times (e.g., vacations). For regular movements during the work week, at least 2 weeks of observation are needed. For more variable movements/times, substantially longer observation periods will be necessary [54]. The need for long-term observation reinforces the need to ensure acceptability of tracking devices by the study population and emphasizes the importance of device wearability (Paz Soldan et al. unpublished, Vasquez-Prokopec et al. unpublished). Data management Although gathering movement information is becoming more feasible, handling movement data remains a challenge [53]. GPS and other devices provide tracks of movements that must be processed into data useable in analyses: for example, the locations visited, frequency of visitation, and time spent during visits. Tools are becoming available to facilitate data processing (e.g., [57],[58],[59]) that integrate with existing GIS and statistical software packages (e.g., Arc-GIS, R). Such tools will facilitate data analysis. Conclusions Because patterns of contact between pathogens and susceptible hosts are heterogeneous, disease interventions can be made more effective and efficient by targeting the key points or ‘nodes’ of transmission [3]. Even where heterogeneous patterns are clearly documented, not knowing the factors driving such patterns impedes one's ability to effectively target control. Is a biting preference toward young adults [60] because they are intrinsically more attractive to a host-seeking mosquito or, because of their behavior, they are more likely to be exposed to mosquitoes? Although many different causes of host-vector contact heterogeneity have been proposed [summarized by 6], variation in exposure due to human behavior is likely to be key across disease systems. The role of other risk factors (e.g., host-preference) will always be conditioned by exposure rates. The study of human movement is thus critical to the identification of key individuals and key locations. Nevertheless, movements have largely been neglected in studies of indirectly transmitted disease even though it is becoming increasingly easy to measure. Quantifying and describing human movements promises more than just characterization of key heterogeneities. Quantification of the collective dynamics of human populations provides information necessary for models intended to predict disease outbreak and spread and to evaluate control alternatives to halt epidemics [8],[35],[51]. Buscarino et al. [61], for instance, predict that movements within a population have an important effect on the epidemic threshold, lowering this as individuals move over larger distances more frequently. Additionally, quantifying movements and applying that information to a variety of diseases creates the opportunity to identify common places where infection occurs across diseases and, thus, the potential to leverage public health programs by allowing limited resources to be targeted to the most important locations for more than one disease. Rigorous examination of the role of human movement across different scales will significantly improve understanding of pathogen transmission, which will be critical to increasing the effectiveness of disease prevention programs. As transmission rates are reduced through intervention efforts, we expect the importance of heterogeneity in exposure to increase and to play an even more important role in pathogen persistence. Characterization of movements will thus not only facilitate the elimination of disease, it will help to prevent its return. Supporting Information Text S1 Calculating individual risk. (0.06 MB DOC) Click here for additional data file. Table S1 Example space time budget from human movements. (0.05 MB XLS) Click here for additional data file.
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            Texas Lifestyle Limits Transmission of Dengue Virus

            Urban dengue is common in most countries of the Americas, but has been rare in the United States for more than half a century. In 1999 we investigated an outbreak of the disease that affected Nuevo Laredo, Tamaulipas, Mexico, and Laredo, Texas, United States, contiguous cities that straddle the international border. The incidence of recent cases, indicated by immunoglobulin M antibody serosurvey, was higher in Nuevo Laredo, although the vector, Aedes aegypti, was more abundant in Laredo. Environmental factors that affect contact with mosquitoes, such as air-conditioning and human behavior, appear to account for this paradox. We conclude that the low prevalence of dengue in the United States is primarily due to economic, rather than climatic, factors.
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              Spatial and Temporal Clustering of Dengue Virus Transmission in Thai Villages

              Introduction Dengue is the leading cause of human arboviral disease worldwide. Dengue viruses (DENV) of the family Flaviviridae and genus Flavivirus, co-circulate as four antigenically related serotypes (DENV-1, −2, −3, and −4), each in varying annual frequencies in Thailand [1] and other tropical countries. The container-breeding mosquito Aedes aegypti (L.) serves as the primary vector responsible for DENV transmission within human populations. Females feed preferentially and frequently on human blood and consequently live in and around human dwellings [2,3]. Transmission of DENV to humans results in either inapparent infection, undifferentiated febrile illness, dengue fever (DF), or life-threatening dengue hemorrhagic fever (DHF). Except for a few notable exceptions, vector control (larvicide treatments, insecticide sprays, and source reduction) has been ineffectively implemented, and no vaccine or clinical cure is yet available for use. Consequently, DENV remain a major cause of morbidity in the tropics and threaten to further expand geographically. DENV transmission and disease are determined by a combination of factors [4] involving the human host [5–7], virus [8–11], mosquito vector [12,13], and environment [13]. Although past studies have revealed general temporal and spatial patterns in the distribution and abundance of Ae. aegypti and human DENV infections [14–18], greater resolution of transmission dynamics across finer geographic and temporal scales is needed to refine current dengue surveillance and control strategies. In an earlier prospective cohort study of schoolchildren in Thailand, Endy and others [19] reported a nonuniform distribution of DENV illness and viral serotypes. To test the hypothesis that DENV transmission is spatially and temporally focal, we extended the school-based study design to include cluster investigations [20] in villages associated with schools. By sampling children and mosquitoes within the neighborhood of children absent from school with fever and dengue viremia, we hypothesized that we would be able to detect, in the same general area and time, other human and mosquito infections and more precisely identify determinants of transmission risk. We used school-based dengue cases to trigger village surveillance of children and mosquitoes within spatial and temporal clusters. We sought a rigorous study of cluster areas over a 15-d period to more accurately define the burden of DENV within a prescribed area (both inapparent and symptomatic infections) and its relationship to mosquito density and infectivity. On the basis of our data, we aimed to consider implications on improving disease prevention strategies. Methods Study Area and Selection of Schools and Villages Our study area (Muang District, Kamphaeng Phet Province [KPP], north-central Thailand [19]) is, by Thai standards, relatively sparsely populated with 233,033 residents in 63,500 houses in an area encompassing 1,962 km2. The average temperature is 28.0 °C with an average monthly rainfall of approximately 200 mm during the rainy months of May to October (National Statistical Office). We selected 11 participating primary schools on the basis of higher numbers of hospitalized dengue cases amongst their students during the prior 5 y, proximity to our field station, and interest of the school administrators. Selected schools (Figure 1) were associated with 32 villages (8,445 houses). Given the workload limitations of entomological surveys, 20 of these villages (4,685 houses) were selected for inclusion on the basis of the density of houses, favoring those with houses in close proximity of each other ( 20–40 m, >40–60 m, >60–80 m, and >80–100 m). In order to evaluate for a distance effect in conjunction with enrollee demographics, a multivariate logistic regression model was formulated. Scientific and Ethical Review and Approval The study protocol and consent forms were approved by the AFRIMS Scientific Review Committee and the ethical review committees of the U.S. Army Surgeon General, Thai MoPH, University of California at Davis, University of Massachusetts Medical School, and San Diego State University. Results Initiation of Cluster Investigations Of the 1,204 febrile children (506 in 2004 and 698 in 2005) who provided blood specimens, 48 (28 in 2004 and 20 in 2005) had detectable DENV viremia. Thirty-four cluster investigations were conducted during the study period (Table 2). Ten clusters (five pairs) in 2004 and two clusters (one pair) in 2005 were spatially and temporally matched. The sex and age distribution of the positive and negative index cases were similar. Children in 58% (seven of 12) of the positive clusters (six in 2004 and one in 2005) attended a single school (school number 2). Table 2 Summary of Cluster Investigations Cluster Enrollees Among the 556 village enrollees (217 in positive and 339 in negative clusters), 27 DENV infections were detected during the 15-d follow-up period. These incident infections occurred exclusively in positive clusters (t-test; p < 0.01; AR = 10.4 per 100; 95% confidence interval [CI] 1–19.8 per 100). This result represented a 4.9% risk among enrollees for experiencing a DENV infection within 15 d of cluster initiation, but a 12.4% risk among enrollees who resided in a positive cluster. Cluster number 4 (Figure 2) contributed disproportionately to this difference. However, all but one positive cluster (cluster number 12) exhibited at least one neighbor with dengue within the 15-d period. There was a statistically significant clustering of DENV cases close to the center of positive clusters when we examined all positive clusters together (Figure 3). Demographics of enrollees between positive and negative clusters were comparable (Table 3). There was no difference in distance between the index cases and respective enrollees in the positive and the negative clusters. Table 3 Comparison of Dengue-Positive and Dengue-Negative Clusters Figure 2 Intense DENV Transmission in Cluster 4 Cluster number 4 illustrates extensive DENV transmission occurring within a 15-d period. In comparison, the paired negative cluster (cluster number 5, not shown) included 22 houses, 21 Ae. aegypti, and 15 contacts with no evidence of DENV transmission within a 15-d period. These index cases were 258 m apart and the cluster investigations were initiated 2 d apart. Figure 3 Clustering of DENV Infections within Positive Clusters This graph shows the relationship of distance between the houses of enrollees and the index case in the positive clusters and the proportion of those enrollees that experienced DENV seroconversion. Error bars represent 95% CIs of the proportions. Numbers in parenthesis indicate the number of positive enrollees and the total number of enrollees in each distance interval. The relationship between distance and the proportion of enrollees that are dengue positive was significant (Fisher's exact test, p < 0.001). A multivariate logistic regression model was estimated to examine the focal nature of transmission while controlling for cluster demographics. Distance between the house of each enrollee and the index case was the measure of focality. An indicator variable was used to account for the evidently excessive transmission in cluster number 4. The model included the age and gender of the enrollees as well as the interaction of these two variables. Resulting coefficient estimates, standard errors, and p-values are given in Table 4. A diagnostic test does not indicate a lack of fit (Hosmer-Lemeshow test, p = 0.23) [30]. A negative and significant parameter estimate indicated that the probability of infection decreased as the distance between enrollees and the index house increased. Modeling results also indicate a gender difference in the effect of age on the probability of infection. The probability that a male enrollee seroconverted decreased with age. This effect was not observed among female enrollees, in whom older enrollees had a higher probability of infection. These trends are apparent in the distribution of infections (Figure S1; Table 5). Table 4 Results of Multivariate Logistic Regression Analysis Table 5 Infections among Enrollees in Positive Clusters by Gender and Age Group Clustering was additionally observed within households as has been previously described [31]. Relative risk of dengue seroconversion among household enrollees of a dengue versus non-dengue case was 2.63 (95% CI 0.96–7.21; Pearson's Chi2 test) with an absolute risk of 6.88 per 100 (95% CI 0–17.29), indicating a strong, but not statistically significant trend towards household risk. Of the 27 DENV infections among village enrollees (Table 6), 14 were inapparent, and 13 were symptomatic. Inapparent infections were more likely with primary (five out of six) than secondary (seven out of 19) DENV infections (p = 0.05; Pearson's Chi2 test). All but one positive cluster (cluster number 6) had concordance of serotypes between the index case and viremic enrollees. (Pearson's Chi2 test used.) Table 6 Clinical Spectrum of Illness among 27 Enrollees with DENV Infections Environmental Determinants of Transmission Among environmental features evaluated ( Table 3), positive clusters were less likely to have piped water than were negative clusters. Though the number of water-holding containers was similar in houses with and without piped water (17.6 ± 8.6 versus 17.8 ± 8.1, t-test, p = 0.28), containers with Ae. aegypti larvae or pupae were significantly less abundant in houses with than without piped water (3.2 ± 3.0 versus 4.4 ± 3.3, t-test, p < 0.001). Use of the larvicide Temephos was higher in the schools than in the villages; 43% and 30% of containers had Temephos in schools in 2004 and 2005, respectively. On average 10% of containers had Temephos in the villages during both study years. Mosquito Collections and Spraying A total of 1,022 adult female Ae. aegypti were collected from within and immediately surrounding homes (Figure 1; Table 2) of which eight (0.8%) were PCR-positive. The average proportion of houses sampled was 0.92 in the positive clusters and 0.93 in the negative clusters (t-test, p = 0.53). Average number of Ae. aegypti pupae/person was significantly higher in positive clusters (Table 3). Although no significant differences were detected, all classical entomological indices (House, Container, and Breteau) and average number of female Ae. aegypti adults/person were higher in positive clusters. The average proportion of houses sprayed was 0.87 in the positive clusters and 0.84 in the negative clusters (t-test, p = 0.39). A total of eight female Ae. aegypti were collected from schools associated with cluster initiation; none were PCR-positive. Discussion Although focal DENV transmission has been noted previously [14,15,32], to our knowledge this is the first study to demonstrate, using control clusters and precise human and entomological data, recent DENV transmission that was focal through space and over a short time span (15 d). DENV-infected hosts (27 enrollees) and vectors (eight Ae. aegypti) were exclusively identified in the 12 dengue-positive clusters, despite a nearly 1:2 ratio of enrollees between positive and negative clusters. Furthermore, we observed significant central clustering of DENV cases within positive clusters. We suspect that focal transmission was associated with recent DENV introductions because of the 217 paired serologic specimens from positive cluster enrollees, only one revealed an elevated but declining immunoglobulin M level, which would be indicative of a recent DENV infection occurring up to 60 d prior to cluster initiation [22]. Consequently, we attributed the observed DENV transmission (enrollees with viremia on day 0 or 15 and/or seroconversion between days 0 and 15) to recent virus introductions. This conclusion is in contrast, however, to data published by Beckett and others [20] who conducted cluster investigations in West Jakarta, Indonesia. They detected 175 recent DENV infections upon enrollment in 53 positive clusters compared to our one in 12 positive clusters, arguing against recent virus introduction. We attribute these contrasting results to study design differences. First, we recruited from schools whereas Beckett recruited from a hospital, potentially after the virus had undergone significant community-based amplification. Second, we preferentially enrolled children as the primary susceptible and amplifying portion of the host population. Beckett additionally enrolled adults. Adults may have exhibited greater background dengue immunity that may have confounded the serologic data. Third, Beckett's study was conducted in an urban area, in contrast to rural villages in our study. Differences in transmission dynamics between these kinds of habitats were likely shaped by the frequency of DENV introductions and diversity in human behaviors. Previous studies have documented hyperendemicity of all four DENV serotypes with an approximate 5% annual risk of acquiring an infection in KPP [19]. In our study, cluster number 4 had a 52% attack rate among enrollees sampled during the 15-d follow-up period. However, after excluding this cluster and its matched negative cluster, the adjusted AR remained high (six per 100). This number represented a 12.4% risk of an enrolled child acquiring a DENV infection within a 15-d period when living within 100 m of a child ill with dengue. Eleven of 12 positive clusters had at least one enrollee with acute dengue in addition to the index case. Given the required intrinsic incubation period, and the finding that all eight virus isolates from mosquitoes matched the serotype recovered from the index case suggest, though not definitively, that except for children from whom virus was recovered on day 15, multiple viremic children within a cluster were infected by one or very few infected mosquitoes. Other evidence within our study to further support village- and not school-based vector sources of DENV infection are that: (1) mosquito populations in schools were extremely low, (2) children seroconverting to dengue within a cluster attended different classrooms within the school, (3) genomic sequences of the envelope (E)-regions of the viruses isolated from children and mosquitoes within the same villages were identical (R.G. Jarman, unpublished data), and (4) housemates of dengue seroconverters had a higher relative risk for DENV infection than those of nondengue seroconverters. The latter observation is consistent with previous reports [14–16]. We suspect that the predominance of DENV transmission in KPP villages reflects, at least in part, routine and effective vector control in schools (insecticide every May and July and Temephos to containers every 3 mo), but not in village homes. Differences in transmission observed between positive and negative clusters could not be attributed to differences in enrollee demographics. Differences in behavioral factors, however, could not be excluded. Within positive clusters, risk of infection decreased with age for males and increased with age for females. This observation merits further investigation with a larger sample and analysis of sex-specific behaviors that might modify risk of infection with advancing age. The only statistically significant determinant among environmental features associated with focal DENV transmission was the greater availability of piped water in negative clusters. Though one may consider a causal relationship (that is, less piped water availability leading to greater need for water storage leading to more containers for larval mosquito development resulting in higher dengue risk), we found no difference in the number of containers between cluster types. Although accurate data on water turn-over are difficult to obtain, the greater number of positive containers in positive than in negative clusters could not be explained by a difference in the frequency of container turn-over rates that we measured. These data could reflect a historical norm or behavior in response to lack of reliability of piped water possibly guided by people's knowledge of dengue preventive measures [33]. The only statistically significant difference among entomological indices was the greater number of Ae. aegypti pupae per person in positive than negative clusters. It is important to note that observed mean pupae per person exceed by an order of magnitude the minimum entomological threshold estimated by Focks and others [34] for a different region of Thailand. This implies that even when pupal densities are relatively high, differences in this measure of entomological risk can be epidemiologically informative. Although adult mosquito population density tended to be higher in positive clusters, differences were not statistically significant, perhaps due to limitations in sampling adult Ae. aegypti with backpack aspirators. Alternatively, mosquito density may be most informative when viewed in concert with herd immunity, and mosquito density alone may be less relevant than the presence of DENV-infected mosquitoes that potentially can transmit virus to multiple individuals [2,3]. Dengue cases in enrollees occurred over a wide range of female Ae. aegypti densities (Figure 4). At densities higher than approximately 1.5 Ae. aegypti females per child, clusters were more likely to be positive than negative. This indicates that DENV transmission was more likely to occur at higher vector densities. Figure 4 Relationship between Vector Density and Dengue Cases Relationship between the number of Ae. aegypti females per child and dengue transmission within 12 positive and 22 negative cluster investigations in 2004 and 2005. Dengue transmission is expressed as the number of positive PCRs on days 0 or 15 of study or of dengue seroconversions between days 0 and 15 per child per cluster. Perifocal spraying is a common approach by health departments to contain/control dengue. However, this practice has been found to be ineffective in aborting DENV transmission [13,35]. Our data suggest that if school-based surveillance can be bolstered by rapid, easy-to-use, and affordable diagnostics, spatially and temporally focused vector control in rural areas such as KPP could be more effectively applied to contain new virus introductions and offset the theoretical risk of longitudinal transmission within and beyond village foci. Although the risk of infection decreased significantly with distance from the center of a cluster, we did not examine people living beyond 100 m of an index case. Our study did not define the spatial dimensions of DENV transmission. Nevertheless, we expect that interventions will need to go beyond a 100 m radius of the home of a DENV-infected child because viremic residents or visitors bitten by an infected mosquito can move virus farther than a flying, infected adult female Ae. aegypti [13,35]. We do not know the longitudinal effects of killing adult mosquitoes on transmission within a community. Koenraadt and others [27] determined in our study area that within 1 wk of spraying insecticide inside homes, approximately 50% of prespraying levels of Ae. aegypti populations were reestablished. Identifying only two of 217 child enrollees with dengue viremia on day 15, both approximately 50 m from the index case within the same positive cluster, indicates that vector control can be locally successful when promptly and properly applied in response to a dengue case. Insecticide applications are most effective when applied inside homes where most Ae. aegypti rest [12] and otherwise avoid contact with insecticides applied outdoors [35–37]. Though our study design was rigorous, our conclusions must be considered in the context of largely logistical limitations: (1) We did not sample all children and mosquitoes within the cluster area. (2) We were unable to characterize the serotype of all DENV infections among village enrollees given restrictions in the frequency of collecting blood from children. (3) We did not collect data on human mobility/behavior that may have influenced the dynamics of transmission within the villages. (4) The possible contribution of adults to DENV transmission was not studied. (5) We did not study the seroprevalence profiles of cluster enrollees. Future studies should focus on positive clusters to more fully characterize the transmission dynamics, the impact of human behavior on transmission patterns, the appropriate spatial scale for disease surveillance/control, and identify more practical and cost-effective approaches to rapid dengue diagnosis. Our cluster methodology provided additional epidemiologic insights. Of note, 14 of the 27 cases of dengue among enrollees were clinically inapparent during this period when DENV-4 was the primary serotype circulating. Most (five of six) primary DENV infections detected in our study were clinically inapparent, similar to observations during a predominantly DENV-2 transmission year in Bangkok [38]. The nearly 1:1 ratio of inapparent to symptomatic secondary DENV infections in our study is also consistent with previous results from KPP [19]. DHF occurred in one (8%) of 12 symptomatic infections and one (4%) of 27 DENV infections confirming that severe dengue represents only a small fraction of the total DENV burden. Future cluster studies can complement these clinical and virologic data by examining correlates of protection that limit transmission, early immunologic events via postinoculation pre-illness specimens and their association with disease severity and sequence variation among viruses through time and space as they circulate between human and mosquito hosts. The prospective cluster methodology utilized here and by others [20] has the potential for broad application. It can be used for multidisciplinary transmission studies of other vector-borne viral diseases as well as spatially and temporally clustered infectious diseases. Supporting Information Figure S1 The Predicted Probability of Infection for Enrollees within Positive Clusters as a Function of Distance to the Index House The probabilities are given for males and females ages 3, 8, and 13 y. Model parameters are reported in Table 5. (51 KB DOC) Click here for additional data file.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, USA )
                1935-2727
                1935-2735
                July 2014
                10 July 2014
                : 8
                : 7
                Affiliations
                [1 ]Virology Department, Centre Pasteur Cameroon, Member of the International Network of Pasteur Institutes (RIIP), Yaounde, Cameroon
                [2 ]Epidemiology Department, Centre Pasteur Cameroon, Member of the International Network of Pasteur Institutes (RIIP), Yaounde, Cameroon
                [3 ]Institut Pasteur du Laos, Laboratoire des Arbovirus et Maladies Virales Émergentes, Vientiane, Lao PDR
                [4 ]Institut Pasteur de Bangui, Bangui, Central African Republic
                [5 ]IRD, UR 016, Montpellier, France
                [6 ]Institut Pasteur de Madagascar, Antananarivo, Madagascar
                [7 ]Institut Pasteur de La Guyane, Laboratoire de Virologie, Cayenne, French Guiana
                [8 ]UMR MIVEGEC (IRD 224 - CNRS 5290 - UM1 – UM2), Institut de Recherche pour le Développement, Montpellier, France
                [9 ]Equipe Ecologie des Systèmes Vectoriels, Centre International de Recherches Médicales de Franceville, Franceville, Gabon
                Institute of Collective Health, Federal University of Bahia, Brazil
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MD CP RP JPH CR DR. Performed the experiments: MD CP RP MG BK DR. Analyzed the data: MD CP PB. Contributed reagents/materials/analysis tools: MD CP RP MG BK DR. Wrote the paper: MD CP PB.

                Article
                PNTD-D-13-02045
                10.1371/journal.pntd.0002950
                4091864
                25009996

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 10
                Funding
                This study was supported by the French government (Agence Nationale pour la Recherche) through the Epidengue project (ANR 05 SEST 010 -01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Microbiology
                Virology
                Population Biology
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Infectious Diseases
                Viral Diseases
                Dengue Fever
                Tropical Diseases
                Neglected Tropical Diseases

                Infectious disease & Microbiology

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